In recent years, with explosive spread of the Internet, various high-speed search engines are used.
The current information retrieving methods are broadly classified into the following two methods.
Full text search: by inputting some keywords, a document including the input keywords is retrieved.
Similarity search: by designating an index search and a document or keyword as an input search key, a document similar to the designated input search key is retrieved.
However, there is limitation in the searches using a so-called keyword and a number of pieces of unnecessary information are hit as a result of a search, so that information which is really needed cannot be obtained. A vague request of the user cannot be grasped.
One of methods of solving the problems uses a vector space model (VSM). According to the method, the presence/absence or the number of appearing times of a word in an input document is used as a feature amount and the degree of similarity between data to be retrieved and an input document is calculated. In the method using the VSM, a feature vector calculating method and a distance between vectors are actively being studied. SMART of the Cornell University, Okapi of City University, INQUERY of the Massachusetts University, and the like are known. According to those methods, however, since a vector is constructed by using frequency of occurrence of a word included in a document as an axis, although a relation to a search keyword is known, semantic feature of the contents of the document and compatibility to the intention of a search of the user cannot be evaluated.
The applicant of the present invention therefore has proposed a high-speed search method using a dependent vector in Japanese Patent Application No. 2001-1365. According to the method, the degree of interest of the user in various attribute groups regarding semantic features of the contents is defined as dependence to form a vector. Consequently, a user request can be quantitatively expressed and both a high-speed search and a similar search adapted to the user can be realized.
However, the search method still has room for improvement with respect to mainly the following points:                1) It is difficult to automatically generate and optimize an inquiry description.        2) It is difficult to automatically generate and optimize a contents description.        
Further, from the viewpoint of adaptation to the user,                3) adaptation of a search process        4) learning and updating of a system are desired.        